code: def conv_only(
      from_layer,
      name,
      num_filter,
      kernel=(1, 1),
      pad=(0, 0),
      stride=(1, 1),
      bias_wd_mult=0.0,
      shared_weight=None,
      shared_bias=None,
  ):
      if shared_weight is None:
          weight = mx.symbol.Variable(
              name="{}_weight".format(name),
              init=mx.init.Normal(0.01),
              attr={"__lr_mult__": "1.0"},
          )
          bias = mx.symbol.Variable(
              name="{}_bias".format(name),
              init=mx.init.Constant(0.0),
              attr={
                  "__lr_mult__": "2.0",
                  "__wd_mult__": str(bias_wd_mult),
              },
          )
      else:
          weight = shared_weight
          bias = shared_bias
          print("reuse shared var in", name)
      conv = mx.symbol.Convolution(
          data=from_layer,
          kernel=kernel,
          pad=pad,
          stride=stride,
          num_filter=num_filter,
          name="{}".format(name),
          weight=weight,
          bias=bias,
      )
      return conv
  
file_path: insightface/detection/retinaface/rcnn/symbol/symbol_mnet.py
imports: import mxnet
  import mxnet.ndarray
  import mxnet.gluon
  import mxnet.gluon.nn
  import mxnet.autograd
  import numpy
  from rcnn.config import config
  from rcnn.PY_OP import rpn_fpn_ohem3
  from rcnn.symbol.symbol_common import get_sym_train
label_desc: Use image node_id 1 for calling a global function with example usage: conv_only(from_layer, name, num_filter, kernel, pad, stride, bias_wd_mult, shared_weight, shared_bias) and returns: conv